To measure the consumption of heat, gas, water or electricity, the corresponding suppliers are more and more turning to electronic devices, in favor of the mechanical consumption meters previously employed.
The consumption meters usually comprise a sensor that, for example in the case of an electricity meter, may consist of a resistor array, and a system for receiving and processing the measuring signals supplied by the sensor, which in the following shall be referred to as the signal processing system.
The signal processing system, on the one hand, must convert the measuring signals delivered by the sensor into real-time data that are representative of the physical magnitudes being measured and, on the other hand, must be able to process the data so acquired, in order, for example, to compute the energy cost by means of a tariff reference table, or to process the data in such a way that they can be fed to a digital display.
In the case of known consumption meters, the signal processing system may consist, for example, of a processor and a memory, where the data required for both the different processing functions, as well as the instructions, are stored.
An example of an electronic water meter using such a signal processing system is described in the application report SLAA138 of Texas Instruments, published in January 2002. To process the sensor signals, a Texas Instruments micro-controller of the MSP430 family is used in this case, which can be connected to a sensor by way of an analog interface and an analog to digital converter. This micro-controller has both a processor and a memory. The program stored in the memory is configured so as to enable the processor to process the data from the sensor, as well as to output the measured result on an LCD display.
Such a system has considerable disadvantages in practical terms, in particular with respect to flexibility when having to deal with changing sensor properties. It could then become necessary that the data and commands stored in the memory of the processor, as far as they are involved in the conversion of the sensor signals into measured data, will have to be changed when the sensor is replaced by a new sensor model. Since the sensors can differ from each other, depending on the conditions prevailing during the production of the sensors, thus making it necessary, for example, to store different calibration data or calibration algorithms for different sensors in the memory of the processor. Individual characteristics of the sensors, such as amplification, characteristic curves or output offset voltages must under certain circumstances be taken into consideration. Filters may also be used to suppress interference carried by the analog sensor signals, so that their influence on the measured signals will also have to be taken into account.
Modifying the data and commands, necessary for the conversion of the sensor signals, is also laborious because the tasks to be handled by the processor must be carried out on two different time bases. Sensor-dependent commands are processed in real time, while commands that are not dependent on the sensor, such as the computation of energy costs by means of an energy tariff reference table of an energy provider, need not be processed in real time and so can be handled at a lower clock rate. It follows that modifications of the data and commands in the memory of the processor, that are not dependent on the sensor output, are usually very labor-intensive to implement.